Multilevel multinomial regression analysis of factors associated with birth weight in sub-Saharan Africa

Birth weight significantly determines newborns immediate and future health. Globally, the incidence of both low birth weight (LBW) and macrosomia have increased dramatically including sub-Saharan African (SSA) countries. However, there is limited study on the magnitude and associated factors of birth weight in SSA. Thus, thus study investigated factors associated factors of birth weight in SSA using multilevel multinomial logistic regression analysis. The latest demographic and health survey (DHS) data of 36 sub-Saharan African (SSA) countries was used for this study. A total of a weighted sample of 207,548 live births for whom birth weight data were available were used. Multilevel multinomial logistic regression model was fitted to identify factors associated with birth weight. Variables with p-value < 0.2 in the bivariable analysis were considered for the multivariable analysis. In the multivariable multilevel multinomial logistic regression analysis, the adjusted Relative Risk Ratio (aRRR) with the 95% confidence interval (CI) was reported to declare the statistical significance and strength of association. The prevalence of LBW and macrosomia in SSA were 10.44% (95% CI 10.31%, 10.57%) and 8.33% (95% CI 8.21%, 8.45%), respectively. Maternal education level, household wealth status, age, and the number of pregnancies were among the individual-level variables associated with both LBW and macrosomia in the final multilevel multinomial logistic regression analysis. The community-level factors that had a significant association with both macrosomia and LBW were the place of residence and the sub-Saharan African region. The study found a significant association between LBW and distance to the health facility, while macrosomia had a significant association with parity, marital status, and desired pregnancy. In SSA, macrosomia and LBW were found to be major public health issues. Maternal education, household wealth status, age, place of residence, number of pregnancies, distance to the health facility, and parity were found to be significant factors of LBW and macrosomia in this study. Reducing the double burden (low birth weight and macrosomia) and its related short- and long-term effects, therefore, calls for improving mothers' socioeconomic status and expanding access to and availability of health care.


Measurement of variables
The study's outcome variable was birth weight, which was classified as low, normal, and macrosomia.We included live births for whom birth weights were recorded.Maternal education status, household wealth status, age, media exposure, sex of the head of the household, women's autonomy in making health care decisions, marital status, wanted child, child's sex, number of pregnancies, parity, distance to health facility, duration of birth interval, number of ANC visits, sub-Saharan African region and residence were the independent variables considered in the study (Table 2).

Data management and analysis
All the analysis was based on the weighted data.Data management and analysis were done using STATA-17 software.The outcome variable (birth weight) has three categories; LBW, normal and macrosomia.
A multilevel multinomial logistic regression model was fitted to examine the association between individual and community-level variables with macrosomia and LBW, using normal birth weight groups as a reference category.Compared to the standard multinomial logistic regression model, the multilevel multinomial logistic regression analysis has advantages.It reduces parameter overestimation and obtain more accurate estimates of the model parameters because the DHS survey is hierarchical.To estimate the variation between clusters, we used clusters/EAs as a random variable.Furthermore, multilevel modelling can estimate cluster-level effects, In the multilevel model, PCV quantifies the overall variation attributable to both individual-and communitylevel factors in contrast to the null model.
In the bivariable analysis, variables with p-value < 0.2 were chosen and considered for the multivariable analysis.In the final model, the Adjusted Relative Risk Ratio (aRRR) with a 95% Confidence Interval (CI) was reported to define the significance of the association.

Ethical consideration
In the case of this study, we have been granted an authorized letter from the measure DHS program for the use the data.DHS is publicly available de-identified data; ethical approval is not needed.

Results
A total of 207,548 live births with birth weight measurements were included in this study.Of them, 121,192 (58.39%) were from rural areas.More than one-fourth (26.29%) of the mothers had no formal education.About 15.59% and 18.21% of the mothers belonged to the poorest and poorest household quintiles, respectively.The majority (66.04%) of the mothers claimed that perceived distance to the health facility was a big problem.Regarding the number of ANC visits, about 100,616 (48.48%) had 4 ANC visits and above (Table 3).

Multilevel multinomial regression analysis results
The ICC indicated that a clustering effect existed, which should be addressed with advanced statistical models such as multilevel modelling to obtain an unbiased standard error and draw meaningful conclusions.The null model's ICC value was 11%, meaning that 89% of the variation in birth weight was attributable to individual variability and that only 11% was caused by cluster variability.Additionally, the final model's PCV value of 0.97 Table 2. List of study variables.
To identify factors associated with birth weight i.e. low birth weight and macrosomia, a multilevel multinomial logistic regression analysis was fitted.Considering the nature of the DHS data, both individual and communitylevel variables were considered as independent variables in the model.
Maternal educational status, household wealth status, parity, women's health care decision-making autonomy, sex of household head, marital status, media exposure, maternal age, occupational status, distance to the health facility, sub-Saharan African region, residence, and number of pregnancies had p-value < 0.2 in the bivariable multilevel multinomial regression analysis and considered for the multivariable multilevel multinomial logistic regression analysis.In the multivariable analysis; maternal educational status, household wealth status, maternal age, parity, number of pregnancies, distance to the health facility, residence, and sub-Saharan African region were significantly associated with low birth weight.Mothers who attained primary education, secondary education, and higher had 10% [RRR = 0.90, 95% CI 0.86, 0.93], 21% [aRRR = 0.79, 0.75, 0.83], and 31% [aRRR = 0.69, 95% CI 0.63, 0.76] lower risk of delivering a low birth weight baby compared to mothers who had no formal education, respectively.The risk of having a low birth weight baby decreases with the higher wealth index; poorer [aRRR = 0.94, 95% CI 0.89, 0.98], middle [aRRR = 0.89, 95% CI 0.85, 0.94], richer [aRRR = 0.85, 95% CI 0.81, 0.89] and richest [aRRR = 0.76, 95% CI 0.71, 0.80] had significant reductions.The risks of having low birth weight baby among respondents aged 25-34 and 35-49 years were decreased by 19% [aRRR = 0.81, 95%CI 0.77, 0.84] and 15% [aRRR = 0.85, 95% CI 0.81, 0.90] compared to mothers aged 15-24 years, respectively.Being multiparous was significantly associated with a decreased risk of delivering a low birth weight baby than primiparous mothers.Regarding the number of pregnancies, mothers who had multiple pregnancies were 8.03 times [aRRR = 8.03, 95% CI 7.64, 8.44] a higher risk of having a low birth weight baby than mothers who had single pregnancy.Being a rural resident increased the risk of delivering a low birth weight baby by 1.14 times [aRRR = 1.14, 95% CI 1.09, 1.18] than their counterparts.The risk of giving a low birth weight baby among women who perceived distance to a health facility as a big problem was 1.06 times [aRRR = 1.06, 95% CI 1.03, 1.10] higher compared to those who perceived it as not a big problem.Compared with the East African region, respondents living in Southern Africa [aRRR = 1.14, 95% CI 1.06, 1.22], and West African regions [aRRR = 1.06, 95% CI 1.01, 1.17] were more likely to have children with low birth weight (Table 5).
In the final multilevel multinomial logistic regression analysis; maternal educational status, household wealth status, maternal age, parity, number of pregnancies, marital status, wanted pregnancy, residence, and sub-Saharan African region were significantly associated with macrosomia.Maternal level of education has a significant association with macrosomia; mothers who attained primary education, secondary education, and higher education were 1.25 [aRRR = 1.25, 95% CI 1.20, 1.31], 1.11 times [aRRR = 1.11, 95% CI 1.06, 1.17] and 1.15 times [aRRR = 1.15, 95% CI 1.04, 1.26] times higher risk of having a macrosomic baby than those who didn't attain formal education, respectively.Mothers in the poorer household wealth [aRRR = 1.06, 95% CI 1.01, 1.12] and Regarding parity and number of pregnancies, the risk of having a macrosomic baby increased as parity increased, and mothers with multiple pregnancies had a lower risk of giving a macrosomic baby [aRRR = 0.47, 95% CI 0.41, 0.54] compared to the singletons.Being rural decreased the risk of macrosomia by 14% [aRRR = 0.86, 95% CI 0.83, 0.97] compared to urban.Compared to the East African region, mothers living in southern Africa and west African regions had a lower risk of delivering a macrosomic baby while those in the Central African region had a higher risk of macrosomia (Table 5).

Discussion
In this study, we investigated into the birth weight-related factors in sub-Saharan Africa, specifically low birth weight and macrosomia.Birth weight was significantly correlated with the following factors: maternal education, household wealth status, maternal age, parity, number of pregnancies, residence, wanted birth, and sub-Saharan Africa region.A significant association was found between low birth weight and macrosomia and the mother's place of residence.Mothers living in a rural area had a higher risk of delivering low birth weight babies in contrast they were at lower risk of giving a macrosomic baby.This was consistent with studies reported in Developing countries 51 , Bangladesh 52 , India 53 , and the United States of America 54 .This might be because reproductive health care services in SSA are highly skewed in urban areas, and therefore rural pregnant mothers have poor access to these health care services, health information related to pregnancy, and nutritional awareness 55,56 .In addition, rural resident pregnant mothers are more susceptible to psychosocial stress, which in turn increases the release of cortisol, and catecholamine, which is linked with low birth weight 57,58 .The risk of giving low birth weight babies was lower among educated mothers than those who didn't have formal education while the risk of macrosomia was higher among educated than those who didn't have formal education.This is consistent with findings reported in Malawi 28 , Brazil 59 , and Eastern Nepal 60 .
Similarly, the risk of having a low birth weight baby was decreased, and the risk of having a macrosomic baby was increased as the household wealth status increased.It was supported by evidence reported in China 61,62 , and Ethiopia 63 .This could be due to pregnant mothers who are less educated are commonly have poor socioeconomic status, which in turn results in poor maternal diet which is responsible for low birth weight 64,65 .In contrast, those who are educated are aware of maternal nutrition like diversified food which is a feature of good household wealth, this might cause excessive pregnancy weight gain and is responsible for increased fetal size 62 .The lower level of education has also been linked with corresponding limited access to maternal health care 66 .We speculated that educated women are more likely to adhere to health messages either because of the cognitive priming that education affords.Another important predictor of low birth weight and macrosomia was multiple pregnancies.It was consistent with study findings in Korea 67 .This could be because multiple pregnancies are identified as high-risk pregnancies, closely linked with a higher risk of maternal and fetal morbidity and mortality 68 .
Studies showed that multiple pregnancies are at increased risk of preterm birth, congenital anomalies, and twin-twin transfusion syndrome 1 .Additionally, multiparity was found to be associated with a lower risk of low birth weight and a higher risk of macrosomia.This was in line with many previous researches [69][70][71] , the possible reason is that multiparous mothers have experience in improving pregnancy outcomes and adhering to pregnancy care.Moreover, advanced maternal age was significantly associated with a lower risk of low birth weight and macrosomia.This was supported by previous studies 25,72 , it could be due to the increased risk of chronic medical conditions like hypertension, and diabetes as well as nutritional depletion could be responsible for the increased risk of low birth weight and macrosomia 73 .
Another significant predictor was pregnancy wantedness, which was consistent with studies reported in Ecuador 74 and Colombia 75 .This could be because mothers with wanted pregnancies have more adhered to maternal health care services like antenatal care and nutritional supplementations 76 .A woman who perceives distance to a health facility as a big problem has a higher risk of delivering a low birth weight baby.It was consistent with study findings in China 77 , Thailand 78 , and India 79 .This could be due to the reason that the healthcare access problem is the main factor for adverse birth outcomes like low birth weight, it highlights that there is www.nature.com/scientificreports/ a need to make maternal healthcare services available and accessible to the community 80 .This study has both strengths and limitations.The present study employed a methodology that utilised the pooled DHS data of 36 sub-Saharan African countries, resulting in a substantial sample size.This could potentially enhance the study's external validity and power.A comprehensive view of SSA can be obtained by utilising a multilevel approach that takes the neighbourhood effect into account.Furthermore, birth weight has been categorised as a binary outcome in earlier research by being assigned the labels LBW/normal.But as you can see, there is a loss of information because macrosomia is a problem that might not be similar to normal birth weight, so treating macrosomia and normal birth weight as normal is not statistically appropriate.Despite the above strengths, the DHS data is cross-sectional, and as such causal relationships cannot be made.Because the retrospective data on their prior history was gathered, it is therefore vulnerable to recall bias.Furthermore, as we conducted a secondary data analysis important variable like maternal medical conditions were not available.

Conclusion
In this study, low birth weight and macrosomia were major public health problems in SSA.We identified several factors associated with low birth weight and macrosomia.Higher level of education, improved wealth, multiparity, multiple pregnancies, perceived distance to a health facility as a big problem, and being a rural resident was significantly associated with low birth weight.Similarly, a higher level of education, improved wealth, multiparity, multiple pregnancies, advanced maternal age, wanted pregnancy, maternal age, and being a rural resident were significant predictors of macrosomia.Therefore, MNCH programs in SSA should target high risk groups the prevention of low birth weight and macrosomia.

Type of place of residence 1
childbirth (0 = 15-24 years, 1 = 25-34 years and 2 = 35-49 years) Sex of child Sex of child (0 = female and 1 = male) Women health care decision making autonomy Person who usually decides on visits to family or relatives (0 = respondent alone, 1 = jointly with husband or partner and 2 = husband or partner or relative alone) Maternal education Education level of mother (0 = no formal education, 1 = primary, 2 = secondary and 3 = higher) Household wealth status Household wealth quintile (0 = poorest, 1 = poorer, 2 = middle, 3 = richer and 4 = richest) Maternal occupation Working status of the mother (0 = not working and 1 = working) Media exposure Media exposure of the mother (0 = have no exposure to all of reading newspaper, listening radio and watching television and 1 = had exposure to either of reading newspaper, listening radio or watching television) Sex of household head Sex of household head (0 = male and 1 = female) Distance to health facility Perceived distance to reach the health facility (0 = not a big problem and 1 = a big problem) Marital status Current marital status of the mother (0 = not married, 1 = married and 2 = divorced/ widowed/separated) Parity Number of children ever born (0 = one, 1 = two-three and 2 = four and above) Number of ANC visits Number of ANC visit for the recent pregnancy (0 = no, 1 = one-three visits and 2 = four and above visits) Sub-Saharan Africa region Sub-Saharan Africa region (0 = East Africa, 1 = Southern Africa, 2 = Central Africa and 3 = West Africa) Duration of birth interval Duration of preceding birth interval (0 = less than 24 months, 1 = 24-59 months and 2 = 60 months and above) Number of pregnancy Number of pregnancy (0 = single and 1 = multiple)

Table 1 .
Sample size in each country, and total sample size in sub-Saharan Africa.
explained approximately 97% of the variation in birth weight.Then four models were fitted and compared using LLR and deviance as they were nested.The final model (a model with individual and community-level characteristics) was the best-fitted model for the data since it had the lowest deviance value (Table Scientific Reports | (2024) 14:9210 | https://doi.org/10.1038/s41598-024-58517-6www.nature.com/scientificreports/

Table 3 .
Descriptive characteristics of the study participants in Sub-Saharan Africa.

Table 5 .
Multilevel multinomial regression analysis of factors associated with birth weight (for both low birth weight and macrosomia) in sub-Saharan Africa.*p-value < 0.05, **p-value < 0.01.**aRRR adjusted relative risk ratio, CI confidence interval.